November 6 | San Jose, California

MATLAB EXPO brought together engineers, researchers, and scientists, who heard real-world examples, saw hands-on demonstrations, and learned about the latest features and capabilities of MATLAB and Simulink.

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Are You Ready For AI? Is AI Ready for You?

AI, or artificial intelligence, is powering a massive shift in the roles that computers play in our personal and professional lives. Most technical organizations expect to gain or strengthen their competitive advantage through the use of AI. But are you in a position to fulfill that expectation; to transform your research, your products, or your business using AI?

Richard Rovner looks at the techniques that compose AI (deep learning, computer vision, robotics, and more), enabling you to identify opportunities to leverage it in your work. You will also learn how MATLAB® and Simulink® are giving engineers and scientists AI capabilities that were previously available only to highly specialized software developers and data scientists.

Richard Rovner, MathWorks

Richard Rovner is Vice President of Marketing for MathWorks, the makers of MATLAB & Simulink. He leads the worldwide marketing organization of 300 people responsible for strategic planning; product and technology strategy; industry, academia, and field marketing; digital marketing; and corporate communications. In this role, Richard has the opportunity to see how MATLAB & Simulink users advance the state-of-the-art in numerous applications and industries. Before joining MathWorks in this role in 2001, Richard held senior marketing and sales positions at SAS. He spent the first part of his career as what is now called a data scientist, working for ten years developing applications in computer vision and image processing, machine learning and artificial intelligence, simulation, and statistical analysis. He has a B.S. in Applied Mathematics from Carnegie Mellon University and an M.S. in Computer Science from George Washington University.

Frank Morese will present an overview of Olea Sensor Networks with a focus on IoT sensor solutions for connected care applications. He will show how the company uses MathWorks technologies to develop unique machine learning algorithms for optimizing sensor performance monitoring for tasks like checking the vital signs of humans and animals without any contact to the body.

Frank Morese, Olea Sensor Networks

Frank Morese founded Olea Sensor Networks to focus on the delivery of advanced multi-sensor IoT network solutions and services for multiple industry sectors through the development of proprietary, patent-pending technologies. In his role since founding Olea in 2005, Frank Morese has been a pioneer in the Internet of Intelligent Things, developing advanced wireless sensor network solutions for the connected care, connected car, and industrial safety IoT sectors, and adding intelligence to the sensor network using multi-sensor data analytics and information fusion technologies.

Frank has held executive leadership positions in key hardware and software systems technology companies including Hughes, Trillium Digital Systems/Intel Corporation, RF Micro Devices, Synopsis, Cadence, and Plantronics. A serial entrepreneur, Frank founded and participated in numerous startups, IPOs, and successful exits, notably the acquisition of Trillium Digital Systems by Intel for $300 Million. At Trillium/Intel, he focused on emerging communication applications for next-generation WAN networks (i.e. wireless cellular, broadband, and VoIP) for the enterprise marketplace as well as infrastructure for service providers.

Frank holds a BSEE from Northeastern University, with post-graduate work at Worcester Polytechnic Institute and The Wharton School of University of Pennsylvania.

Creating Deep Learning-Based Speech Products in Record Time

In the past two years, we’ve seen the industry discover speech as a critical interface protocol between humans and machines, especially for cloud-based information queries driven by speech recognition. However, speech recognition is just the tip of the iceberg. A whole new set of functions—speech enhancement, speaker identification and authentication, and background noise classification—are becoming available. These create new and significant opportunities for every application that touches audio or video—opening new potential for improved intelligibility, personalization, and customer “stickiness.”

BabbleLabs Clear Cloud is an example of a breakthrough deep learning technology applied to widely applicable speech APIs and it gives us a sense of the future roadmap of speech-centric applications. The number of speech problems BabbleLabs is working on is growing by the day, and the company has to develop a flow that will maximize the speed of creating production-ready SW IP. Using mature and comprehensive toolboxes from MathWorks, such as DSP System Toolbox™, Neural Network Toolbox™ (Deep Learning), and MATLAB Coder™, BabbleLabs can create state-of-the-art SW IP products in record time. These SW IP products integrate advanced digital signal processing (DSP) and sophisticated deep learning architectures using a homogeneous flow from development to deployment.

Samer Hijazi, Ph.D., BabbleLabs

Samer Hijazi is a lifelong machine learning and signal processing researcher and product developer. He holds degrees from Kansas State (Ph.D.), Lehigh University (M.B.A), South Dakota State (MSEE), and University of Jordan (B.Sc.), with specialties in machine learning, image processing, and communications DSP. He has worked at the potent intersection of these technologies for over a dozen years at Qualcomm, Philips Research, Agere Systems, LSI Logic, Tensilica, and Cadence. Most recently, he spearheaded Cadence’s efforts to establish a highly respected position in neural networks and, as engineering group director, established a core team of more than a dozen researchers focused exclusively on deep learning technologies. He holds more than 30 patents.

Unleashing the Power of FPGAs Through Model-Based Design

Model-Based Design has long been the de facto standard for algorithm developers for exploring and implementing applications such as software defined radios, embedded vision, motor control systems and medical devices. Many of these applications require high performance compute and benefit significantly from the massively parallel architecture of FPGAs. But to leverage FPGAs, developers needed to bridge the gap between the algorithm-centric world of MATLAB and Simulink and the hardware-centric world of FPGAs, which once required fairly arduous manual translation steps.

Almost 20 years ago Xilinx pioneered the solution to this problem with System Generator for DSP which enabled a Model-Based design flow that could map directly to FPGAs. This has been used successfully over thousands of designs. But a lot has changed over the last 2 decades. New applications such as ADAS, 5G and Machine Learning have placed increasing performance demands on systems and driven the evolution of FPGAs into new device classes such as programmable SoCs and just recently, adaptive compute acceleration platforms (ACAP). Along with that, the model-based programming model has also evolved and moved to higher levels of abstraction in order to manage the massive increase in system complexity.

This talk draws inspiration from the past 20 years of Model Based design to lay a foundation for the next 20 years of innovation. We describe how the market trends, programmable devices and model-based development have changed over the past decade and how they are likely to evolve in the years to come.

Nabeel Shirazi, Ph.D., Xilinx Inc.

Nabeel Shirazi is a Senior Director at Xilinx Inc. He was one of the initial developers of System Generator for DSP in 1998, helped take it to market, and later took over full responsibility for the tool 2005. In 2013, his team launched Vivado IP Integrator which is has become a de facto standard design tool for Zynq / Zynq UltraScale+ embedded designers. In 2017, he was the co-inventor of Model Composer which is Xilinx’s next generation Model-Based design tool.

He holds a Ph.D. in Computing from Imperial College in London where his dissertation was on Automating Production of Run-Time Reconﬁgurable Designs. His MSEE, completed at Virginia Tech, was on a ground-breaking work on Implementation of a 2-D Fast Fourier Transform on a FPGA-Based Custom Computing Platform which led to one of the first implementations of arbitrary floating-point arithmetic on FPGAs. He has 42 patents, and his team has been nominated four times for the technical innovation of the year award at Xilinx and won twice.

Technical Computing and Data Analytics

Predictive Maintenance: From Development to IoT Deployment

Creating Deep Learning-Based Speech Products in Record Time

Master Class: Solving Optimization Problems with MATLAB

Systems Modeling and Control Design

Verification and Validation: Automating Best Practices to Improve Design Quality